Smart Disaster Management and Prevention using Reinforcement Learning in IoT Environment

Lonkar, Yogesh S.; Bhagat, Abhinav Sudhakar; Manjur, Sd Aasif Sd

doi:10.1109/icoei.2019.8862602

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The model in [63] uses multiple sensors connected to analog to digital converter (ADC) like temperature, moisture, water level, and CO level. The data is sent to a raspberry-pi device in digital form.…”

Section: Applications Of Machine Learning Models In Predisaster Manag...mentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

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Section: Applications Of Machine Learning Models In Predisaster Manag...mentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Similarly, Q-learning was selected for its good tradefoff between flexibility and complexity in an adaptive power management for IoT system-on-hips in [28]. Q-learning was also used in an IoT-enabled smart disaster management owing to its ability to adapt to the ever changing and complex world [29].…”

Section: Reinforcement Learning Approachmentioning

confidence: 99%

Digital Twin-Driven Decision Making and Planning for Energy Consumption

Fathy

Jaber

Nadeem

2021

JSAN

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The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

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